Reposting my comment from https://news.ycombinator.com/item?id=40584237 https://news.ycombinator.com/item?id=40584237 : > AGI by 2027 is strikingly plausible.

Reposting my comment from https://news.ycombinator.com/item?id=40584237 https://news.ycombinator.com/item?id=40584237 : > AGI by 2027 is strikingly plausible. GPT-2 to GPT-4 took us from ~preschooler to ~smart high-schooler abilities in 4 years. Tracing trendlines in compute (~0.5 orders of magnitude or OOMs/year), algorithmic efficiencies (~0.5 OOMs/year), and “unhobbling” gains (from chatbot to agent), we should expect another preschooler-to-high-schooler-sized qualitative jump by 2027. The largest GPT-2 model had 1.5 billion parameters (each a connection weight between two artificial neurons). By some estimates, GPT-4 has on the order of 1 trillion parameters, or around three orders of magnitude (10^3) larger.[a] Another jump of three orders of magnitude would require a model with on the order of 1000 trillion = 1 quadrillion parameters. It's far from certain that such scaling is possible over the next three years, and also whether existing model architectures, e.g., Transformers, can continue to improve qualitatively at similar rates as in the recent past, because there is some evidence they're hitting diminishing returns. Prominent experts like Yann LeCun think we will need new ideas and breakthroughs, i.e., new kinds of AI models not yet invented, the timing of which cannot be predicted in advance, by definition.[b] The OP's "forecast" all but ignores these challenges. --- [a] https://en.wikipedia.org/wiki/GPT-4 https://en.wikipedia.org/wiki/GPT-4 [b] https://www.reddit.com/r/singularity/comments/1d5b57b/lecun_tells_phd_students_there_is_no_point/ https://www.reddit.com/r/singularity/comments/1d5b57b/lecun_...